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Classical Decomposition omzet product Y

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sun, 15 Aug 2010 16:22:14 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Aug/15/t1281889318dndi0qz2am5j2sn.htm/, Retrieved Sun, 15 Aug 2010 18:21:58 +0200
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Aug/15/t1281889318dndi0qz2am5j2sn.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
118 117 116 114 112 111 112 114 115 115 116 118 126 131 122 124 119 112 109 108 117 122 127 124 129 141 127 133 114 98 93 101 111 128 126 134 140 158 144 146 138 119 113 120 127 141 144 150 156 174 163 167 160 141 132 144 155 164 162 181 187 209 189 201 193 177 159 158 155 164 163 185 191 217 193 192 184 166 145 146 138 149 145 166
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1118NANA9.49305555555556NA
2117NANA25.875NA
3116NANA10.1597222222222NA
4114NANA13.9305555555555NA
5112NANA4.32638888888889NA
6111NANA-12.0416666666667NA
711295.5347222222222115.166666666667-19.631944444444416.4652777777778
811499.75116.083333333333-16.333333333333314.2500000000000
9115105.1875116.916666666667-11.72916666666679.81250000000003
10115113.777777777778117.583333333333-3.805555555555571.22222222222226
11116114.111111111111118.291666666667-4.180555555555551.88888888888889
12118122.5625118.6253.93750000000001-4.56249999999999
13126128.034722222222118.5416666666679.49305555555556-2.03472222222221
14131144.041666666667118.16666666666725.875-13.0416666666667
15122128.15972222222211810.1597222222222-6.1597222222222
16124132.305555555556118.37513.9305555555555-8.30555555555556
17119123.451388888889119.1254.32638888888889-4.45138888888887
18112107.791666666667119.833333333333-12.04166666666674.20833333333333
19109100.576388888889120.208333333333-19.63194444444448.42361111111113
20108104.416666666667120.75-16.33333333333333.58333333333336
21117109.645833333333121.375-11.72916666666677.3541666666667
22122118.152777777778121.958333333333-3.805555555555573.84722222222224
23127117.944444444444122.125-4.180555555555559.05555555555556
24124125.270833333333121.3333333333333.93750000000001-1.27083333333333
25129129.576388888889120.0833333333339.49305555555556-0.576388888888886
26141145119.12525.875-4
27127128.743055555556118.58333333333310.1597222222222-1.74305555555553
28133132.513888888889118.58333333333313.93055555555550.486111111111143
29114123.118055555556118.7916666666674.32638888888889-9.11805555555554
3098107.125119.166666666667-12.0416666666667-9.12499999999999
3193100.409722222222120.041666666667-19.6319444444444-7.40972222222221
32101104.875121.208333333333-16.3333333333333-3.87499999999999
33111110.895833333333122.625-11.72916666666670.104166666666671
34128120.069444444444123.875-3.805555555555577.93055555555556
35126121.236111111111125.416666666667-4.180555555555554.76388888888887
36134131.229166666667127.2916666666673.937500000000012.77083333333333
37140138.4930555555561299.493055555555561.50694444444443
38158156.5130.62525.8751.5
39144142.243055555556132.08333333333310.15972222222221.75694444444443
40146147.222222222222133.29166666666713.9305555555555-1.22222222222223
41138138.909722222222134.5833333333334.32638888888889-0.909722222222229
42119123.958333333333136-12.0416666666667-4.95833333333334
43113117.701388888889137.333333333333-19.6319444444444-4.70138888888889
44120122.333333333333138.666666666667-16.3333333333333-2.33333333333334
45127128.395833333333140.125-11.7291666666667-1.39583333333331
46141137.986111111111141.791666666667-3.805555555555573.01388888888891
47144139.402777777778143.583333333333-4.180555555555554.59722222222223
48150149.354166666667145.4166666666673.937500000000010.645833333333343
49156156.618055555556147.1259.49305555555556-0.618055555555571
50174174.791666666667148.91666666666725.875-0.791666666666657
51163161.243055555556151.08333333333310.15972222222221.75694444444446
52167167.138888888889153.20833333333313.9305555555555-0.138888888888857
53160159.243055555556154.9166666666674.326388888888890.756944444444457
54141144.916666666667156.958333333333-12.0416666666667-3.91666666666666
55132139.909722222222159.541666666667-19.6319444444444-7.9097222222222
56144145.958333333333162.291666666667-16.3333333333333-1.95833333333334
57155153.104166666667164.833333333333-11.72916666666671.89583333333334
58164163.527777777778167.333333333333-3.805555555555570.4722222222222
59162165.944444444444170.125-4.18055555555555-3.94444444444443
60181176.93751733.937500000000014.06250000000003
61187185.118055555556175.6259.493055555555561.88194444444446
62209203.208333333333177.33333333333325.8755.79166666666671
63189188.076388888889177.91666666666710.15972222222220.923611111111086
64201191.847222222222177.91666666666713.93055555555559.15277777777777
65193182.284722222222177.9583333333334.3263888888888910.7152777777778
66177166.125178.166666666667-12.041666666666710.875
67159158.868055555556178.5-19.63194444444440.131944444444485
68158162.666666666667179-16.3333333333333-4.66666666666669
69155167.770833333333179.5-11.7291666666667-12.7708333333333
70164175.486111111111179.291666666667-3.80555555555557-11.4861111111111
71163174.361111111111178.541666666667-4.18055555555555-11.3611111111111
72185181.645833333333177.7083333333333.937500000000013.35416666666669
73191186.159722222222176.6666666666679.493055555555564.8402777777778
74217201.458333333333175.58333333333325.87515.5416666666667
75193184.534722222222174.37510.15972222222228.46527777777777
76192186.972222222222173.04166666666713.93055555555555.0277777777778
77184175.993055555556171.6666666666674.326388888888898.00694444444449
78166158.083333333333170.125-12.04166666666677.91666666666666
79145NANA-19.6319444444444NA
80146NANA-16.3333333333333NA
81138NANA-11.7291666666667NA
82149NANA-3.80555555555557NA
83145NANA-4.18055555555555NA
84166NANA3.93750000000001NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Aug/15/t1281889318dndi0qz2am5j2sn/1fpok1281889330.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/15/t1281889318dndi0qz2am5j2sn/1fpok1281889330.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/15/t1281889318dndi0qz2am5j2sn/2fpok1281889330.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/15/t1281889318dndi0qz2am5j2sn/2fpok1281889330.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/15/t1281889318dndi0qz2am5j2sn/38h6n1281889330.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/15/t1281889318dndi0qz2am5j2sn/38h6n1281889330.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/15/t1281889318dndi0qz2am5j2sn/48h6n1281889330.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/15/t1281889318dndi0qz2am5j2sn/48h6n1281889330.ps (open in new window)


 
Parameters (Session):
 
Parameters (R input):
par1 = additive ; par2 = 12 ;
 
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
 





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Software written by Ed van Stee & Patrick Wessa


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